An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model

Artificial intelligence (AI) holds immense promise for K-12 education, yet understanding the factors influencing students’ engagement with AI courses remains a challenge. This study addresses this gap by extending the technology acceptance model (TAM) to incorporate cognitive factors such as AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX), alongside human–computer interaction (HCI) elements like user interface (UI), content (C), and learner-interface interactivity (LINT) in the context of using generative AI (GenAI) tools. By including these factors, an expanded model is presented to capture the complexity of student engagement with AI education. To validate the model, 210 Chinese students spanning grades K7 to K9 participated in a 1 month artificial intelligence course. Survey data and structural equation modeling reveal significant relationships between cognitive and HCI factors and perceived usefulness (PU) and ease of use (PEOU). Specifically, AIIM, AIRD, AICF, UI, C, and LINT positively influence PU and PEOU, while AIAX negatively affects both. Furthermore, PU and PEOU significantly predict students’ attitudes toward AI curriculum learning. These findings underscore the importance of considering cognitive and HCI factors in the design and implementation of AI education initiatives. By providing a theoretical foundation and practical insights, this study informs curriculum development and aids educational institutions and businesses in evaluating and optimizing AI4K12 curriculum design and implementation strategies.


External variables
Learning cognition of AI Based on the cognitive characteristics of students in AI education for K-12 (AI4K-12), we propose the inclusion of four key variables to enhance the technology acceptance model (TAM) study: AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX).
This suggestion stems from a comprehensive review of teachers' AI cognition and their willingness to teach AI, highlighting the importance of understanding AIAX, its impact on social welfare, attitude towards use (ATT) AI, perceived teaching confidence, AICF, AI correlations, AIRD, and behavioral intentions (BI).Furthermore, in the context of AI4K12, students' cognition plays a crucial role in influencing their learning process and outcomes.Our research on developing and evaluating AI courses confirms the significance of learning perception abilities, including motivation, confidence, attitude, readiness, and anxiety, in shaping effective AI education strategies.
AI intrinsic motivation (AIIM): Previous study has shown that motivation can enhance students' willingness to learn [50][51][52] .Intrinsic motivation possesses a psychological cognitive process of exploration, experimentation, curiosity, and manipulation, which is a natural manifestation of human learning and integration of knowledge 53 .The intrinsic motivation of learning guides students to set learning goals and continuously participates in the learning process through the classroom learning activities, which has a positive impact on academic performance 54 .Therefore we propose the following assumptions.a. H1a, students' AIIM has a positive impact on their PU in learning AI courses through the GenAI tool.b.H1b, students' AIIM has a positive impact on their PEOU in learning AI courses through the GenAI tool.AI readiness (AIRD): the technology readiness index (TRI) is used to measure people's tendency to accept and use advanced information technology 55 .Based on positive expectations for the use of technology, preparatory work can predict learning behavior 56 .AIRD can measure students' understanding of the comfort level of AI knowledge and technology in their learning and life, and has a related impact on their learning attitude towards AI courses 57 .In the behavioral research of teachers teaching AIED, AIRD is related to BI, and PU has a positive impact on BI.Therefore, we propose the following hypothesis.
iii.H2a, students' AIRD has a positive impact on their PU in learning AI courses through the GenAI tool.iv.H2b, students' AIRD has a positive impact on their PEOU in learning AI courses through the GenAI tool.AI confidence: in AIED, AICF represents students' confidence in learning AI course content 58 .AICF can affect students' willingness to learn and other variables, and is an important impact factor on AI usage behavior [59][60][61] .In research on students using mobile devices for learning, it has been found that mobile device usage confidence has a positive impact on PEOU 62,63 .Therefore, we propose the following assumptions.e. H3a, students' AICF has a positive impact on their PU in learning AI courses through the GenAI tool.f.H3b, students' AICF has a positive impact on their PEOU in learning AI courses through the GenAI tool.AI anxiety: computer phobia is defined as the fear and anxiety of advanced technology 64 .When using mobile devices for learning, mobile device anxiety can also affect learning behavior 63 .Based on the background of AI, AIAX can be traced back to technology phobia and computer anxiety.Define AIAX as a fear of AI, and users' concerns about the unknown impact of AI programs and related technological developments on humans and society 65,66 .In the use of ChatGPT, AIAX predicts learning behavior 67,68 , and the unease of GenAI usage affects user behavior 69,70 .In e-learning environments, where learners interact with AI tools during the process, anxiety and uneasiness affect the user's usage.AIAX has an impact on PU 71 .In using the GenAI tool to learn AI courses, we propose the following assumptions.g.H4a, students' AIAX has a negative impact on their PU in learning AI courses through the GenAI tool.h.H4b, students' AIAX has a negative impact on their PEOU in learning AI courses through the GenAI tool.

HCI factors in AIED
HCI refers to the interaction between users and computers.And human-computer interaction refers to the computer-mediated dialogue that users engage in the created environment by themselves.Interactivity in online educational programs refers to the relationship between students and computers in a human-computer interaction environment 72 .During the process of using the GenAI tool for AIED, HCI has an impact on students' attitudes and behaviors 67 .Therefore, based on the teaching characteristics of using the GenAI tool in AI4K12, we suggest that considering HCI factors and using interface design (UI), content (C), and learner interface interactivity (LINT) as variables to expand TAM research.using interface design (UI): in HCI, an interface is defined as the visible part of the information system that can be touched, heard, and seen by the user 72 .UI is an important factor in the software development process, and user demand oriented design is the key to UI 73 .The emergence of user centered UI principles provides a theoretical basis for designers to conduct UI, such as distinguishing the most important information, buttons with consistent styles, and actively providing feedback 74,75 .In the research field of online courses or mobile applications for learning, following UI principles makes the system easier for students to use and operate, and UI also plays an important role in the system's PU, Based on the technology acceptance model, learning content quality, content design quality, interactivity, functionality, user interface design, accessibility, personalization, and responsiveness are the main factors influencing the acceptance of mobile learning 76,77 .In the process of using GenAI for teaching, the UI also has an impact on PEOU.Therefore, we propose the following assumptions.i. H5, the UI of the GenAI tool used in AI course learning has a positive impact on PEOU.
Content (C): C is related to the course content.In the field of mobile devices, C is considered to have a significant impact on student satisfaction 78 .In the computer context, the structure and capacity of C have a direct impact on PU, and C is an important influencing factor for user acceptance of the system 79 .When investigating the factors that affect the use of BI on mobile devices, C has a positive impact on PU 63 .In evaluating the role of MOOC acceptance and use, C is positively correlated with PEOU 80 .Based on previous research findings, we propose the following assumptions.j.H6a, the use of GenAI tools for teaching's C has a positive impact on students' PU in learning AI courses.k.H6b, the use of GenAI tools for teaching's C has a positive impact on students' PEOU in learning AI courses.
Learner interface interactivity (LINT): LINT allows users to interact with the system through the menu bar using the program 81 .When testing the impact of enhancing student interactivity on improving e-learning acceptance and the relationship between variables, there is a relationship between LINT, PU, and PEOU 29 .During the use of GenAI tools, LINT also has an impact on students' PU and PEOU, so we assume that.
xii.H7a, LINT has a positive impact on students' PU in learning AI courses through the GenAI tool.xiii.H7b, LINT has a positive impact on students' PEOU in learning AI courses through the GenAI tool.

Internal variables
Perceived Usefulness (PU) is defined as the degree to which a user believes that using a specific system will improve their/her work performance.In addition, perceived ease of use (PEOU) is defined as the degree to which users do not need to put in any effort to use the system 43,44 .The correlation between TAM model structures has been proven in many studies.The relationship between PU and PEOU has also been confirmed in research in the field of education.Attitude towards use (ATT) is a person's perception of technology, which is a psychological feedback of liking, enjoying, and being happy with technology 58 , usability, which has a positive impact on the practical use of m-learning systems 82 .In the previous research, there is a higher education students to adopt the meta-educational intention of the factors 83 .and studies on users' sustained intention towards e-learning 84 have both concluded that both PU and PEOU affect a person's ability to use the system's ATT.Therefore, when studying the influencing factors of students' attitudes towards AI teaching using GenAI, we propose the following assumptions: n.H8, the PU of AI courses learned by students through the GenAI tool has a positive impact on ATT.o.H9, students' learning of AI courses through the GenAI tool has a positive impact on PU through PEOU.p. H10, the PEOU of students learning AI courses through the GenAI tool has a positive impact on their attitude towards ATT.

Research model
This study analyzed the learning cognitive and human interaction factors that affect students' attitudes.Expand Davis' TAM model with external variables from literature review and previous research findings.Using PU, PEOU, and ATT as basic variables, seven external variables were derived through literature review and previous research analysis.Figure 1 shows the proposed hypothesis model.This study endeavors to delve into the determinants shaping K-12 students' perceptions of AI courses facilitated by generative AI (GenAI) tools.To elucidate these factors, an analytical framework was formulated, drawing inspiration from Davis' technology acceptance model (TAM) as its foundational underpinning.Building upon the core constructs of TAM-perceived usefulness (PU), perceived ease of use (PEOU), and attitude towards use (ATT)-the research extends the model by incorporating additional external variables gleaned from an exhaustive literature review and synthesis of prior research.Specifically, the model integrates cognitive factors associated with AI learning, including AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX), as well as human-computer interaction (HCI) elements such as user interface (UI), content (C), and Learner Interface Interactivity (LINT).Figure 1 depicts the proposed hypothesis model, illustrating the interconnections among these variables.For participant selection, a convenience sampling approach was adopted to recruit a cohort of 210 Chinese K-12 students spanning grades K7-K9.This sampling method was chosen for its practicality and ease of access, facilitating the efficient enlistment of participants from the target demographic.Demographic details, encompassing age, gender, and grade level, were gathered to furnish insights into the profile of the sample, enabling a more nuanced analysis of the research outcomes.
In terms of tool development and validation, all research instruments utilized in this study were selected or adapted from established measures drawn from prior research endeavors.Rigorous attention was dedicated to ensuring the reliability and validity of these measures, with necessary adjustments made to align them with the study context.Validation procedures encompassed pilot testing and expert validation to affirm the appropriateness of the measures in assessing the intended constructs.Through this meticulous validation process, the research instruments were deemed apt for capturing the pertinent variables of interest.
Data analysis procedures entailed the utilization of structural equation modeling (SEM) techniques to analyze the quantitative data collected through surveys.This analytical approach facilitated the testing of the stipulated hypotheses and the exploration of the relationships between the variables delineated in the research model.Statistical software packages such as SPSS and AMOS were employed to conduct the analyses, enabling robust statistical testing and elucidation of the research findings.By organizing the methodology section in a cohesive narrative format, this study offers a lucid and transparent depiction of the research design, participant recruitment approach, measurement instruments, and data analysis protocols, ensuring rigor and validity in the study's outcomes.

Participants and experimental procedures
The participants in this study were 210 students selected from two high schools in China.Among them, 97 were males (45.7%) and 114 were females (54.3%).The students' grades are K7-K9.Students voluntarily participate in research experiments and are aware of the research procedures.The data related to the experiments are anonymous and have also received permission and recognition from their parents and the school.Students will participate in a one month course, which mainly focuses on AI knowledge learning using the GenAI tool.The main content of the course is the creation of AI visual narratives (story picture books).All students are undergoing systematic AIED for the first time.The experimental process is shown in Fig. 2.
Sample population: the sample population for this study consisted of K-12 students from various schools in China.These students were chosen to represent a diverse demographic, including different grade levels and socioeconomic backgrounds, to ensure the findings were applicable across a broad range of contexts.
Sampling technique: a stratified random sampling technique was employed to select participants for the study.Schools were stratified based on geographic location, school type (public/private), and grade level.Within each stratum, a random sample of schools was selected, and then students within those schools were randomly chosen to participate in the study.This sampling technique helped ensure that the sample was representative of the target population and minimized selection bias.
Justification of sample size: the sample size of 210 Chinese K-12 students was determined based on power analysis and the requirements for structural equation modeling (SEM) analysis.Prior research suggests that a sample size of at least 200 participants is adequate for SEM analysis, particularly when examining complex relationships among variables.Additionally, power analysis was conducted to ensure that the sample size was sufficient to detect meaningful effects with a reasonable degree of confidence.This sample size also allowed for subgroup analyses based on demographic variables such as grade level and gender, providing further insights into potential variations within the sample population.www.nature.com/scientificreports/

Experimental implementation and feedback
The course spans one month, comprising a total of eight classes, dedicated to the creation of AI picture books centered on "AI, Love, and the Future".From sessions 3 to 7, students delve into utilizing ChatGPT, Midjourney, and AI translation software for crafting their picture books.Collaboratively, teachers and students explore the nexus between AI and our world, leveraging GenAI for acquiring novel knowledge.This encompasses mastering AI translation software for bilingual tasks and harnessing generative chat tools for narrative continuity.Additionally, understanding how generative image systems operate in image creation and story coherence is emphasized.
The final session involves student presentations, fostering discussions and idea exchanges between teachers and students.Course content design adheres to input from five AIED experts, detailed in Table 1.
During the course implementation process, students use ChatGPT and Midjourney to create and showcase their works, as shown in Fig. 3 of the course implementation process.

Questionnaire design
The survey instrument is divided into two parts.The first part of the survey questionnaire includes demographic questions, including gender, grade; and the second step uses 38 items to measure the 10 structures of the research model.Ten structures are classified as external variables and internal variables.
External variables (AIIM, AIRD, AICF, AIAX, UI, C, LINT).Internal variables (PU, PEOU, ATT).Each construct is measured by multiple items.In order to obtain participants' responses and quantify the construction, a five-point Likert scale was used to score the questionnaire responses.The Likert scale consists of five answer options, ranging from "strongly disagree" (mapped to number 1) to "strongly agree" (mapped to number 5).This tool was developed after reviewing research on TAM models, AI learning cognitive factors, and HCI factors.All items in the survey questionnaire were proofread by translation experts and translated into Chinese.The specific content and reference materials of the variable item survey questionnaire are shown in Table 2.  Demographic information is summarized in Table 3.
All methods were performed in accordance with relevant ethical guidelines and regulations, the experimental protocols were approved by the Academic Committee of Guangzhou University of Technology and Guangzhou University of Technology, and the experiments were conducted with the informed consent of all subjects and their legal guardians.

Data analysis methods
This study used SPSS 26 and SMART PLS 4.0 for data analysis.Data analysis includes two steps, reliability and validity analysis, as well as hypothesis testing.Firstly, internal consistency reliability (Cronbach's α and composite reliability} was measured by SMART PLS 4.0; and composite reliability (CR) was tested using SPSS 26.High CA and CR values indicate high reliability of the tool.It is recommended that the CA and CR values be higher than 0.70.To evaluate the convergence effectiveness of the construction, we used CR values and average variance extraction (AVE) values, and verified the discriminant effectiveness of the construction by analyzing the square root value of the extracted mean difference (AVE).If all constructs are higher than the correlation between constructs, then the sufficiency of discriminative validity is demonstrated.Secondly, after obtaining

Experimental bias
To mitigate the potential effects of common method bias, several strategies were employed throughout the data collection and analysis processes.First, we ensured anonymity and confidentiality in the survey responses to encourage participants to provide honest and accurate answers without fear of judgment or repercussion.Additionally, we employed procedural remedies such as counterbalancing the order of questionnaire items and using reverse-coded items to minimize response bias.Furthermore, we conducted Harman's single-factor test to assess the extent of common method bias in our data.The results indicated that no single factor accounted for the majority of the variance, suggesting that common method bias was not a significant concern in our study.However, we acknowledge that these measures may not completely eliminate common method bias and have included this limitation in our discussion.

Results
During the course implementation process, students use ChatGPT and Midjourney to create and showcase their works, as shown in Fig. 3 of the course implementation process.

Results of reliability and effectiveness testing
Table 4 shows the reliability analysis results by SPSS 26, and the Clonbachα values meet the standard, all greater than 0.8.Therefore, it can be proven that the research results of variables are reasonable.To ensure the accuracy of measurement results, reliability analysis needs to be conducted on the valid data in the questionnaire before analysis.
Secondly, KMO and Bartlett tests were conducted to analyze the effectiveness of the entire questionnaire.The results are shown in Table 5 below.
From the Table 5, it can be seen that the KMO value is 0.880, and the KMO value is greater than 0.8, which illustrate the research data is very suitable for extracting information.

Discriminant validity
The results of the discriminant validity test are shown in Table 6.It can be seen that the AVE extracted square root (number on the diagonal) of each variable is greater than the correlation between this variable and other variables, so the data is considered to have good discriminant validity.
According to Table 7, the above HTMT values are all below 0.85, indicating that the data has good discriminant validity.

Model fitting index
The initial step in hypothesis testing involves assessing the structural model.Our model adheres to established fitting standards, with all model fitting index values deemed acceptable, including VIF < 5 and F2 > 0.02.Notably, all VIF values fall below 5, signifying the absence of significant collinearity concerns within the dataset.

Hypothesis testing
The sample size of this study is an important factor in model analysis.Therefore, after strict screening, 197 valid questionnaires were used for research analysis, which meets the sample size required for SMART PLS analysis.

The impact of AI learning cognitive factors on PU and PEOU
The results of the study showed that firstly, AIIM had a positive impact on PU and it was the second most influential factor (0.211) on student acceptance as well as positively affecting PEOU.
Although AIRD has a positive effect on PU (0.152) and PEOU (0.136), its positive impact on PEOU is indeed the smallest.This is consistent with the research of Chiu et al. 24 .whichpreviously believed that the level of AIRD can measure the understanding of AI knowledge and technology.
AICF is positively correlated with PU (0.158) and PEOU (0.159), which is consistent with the results of graduate students using mobile devices for learning (Stavros A. Nikou, et al.).The greater the confidence in learning AI, the better students can accept AI courses and maintain sustainable learning behavior.
AIAX has a negative impact on both PU (− 0.130) and PEOU (− 0.162).This is consistent with the results of Tae Hyun Baek and Minseong Kim's study on students' learning behavior using ChatGPT 67 , and also with the results of Stavros A. Nikou et al. 's study on mobile device use anxiety.

Positive impact of HCI factors on PU and PEOU
Among the HCI factors, UI is positively correlated with PEOU, with a path coefficient of 0.173.UI is considered an important influencing factor in online course student acceptance (AL-Sayid, F. and Kirkil, G.) 29 and mobile learning acceptance 77 .While using GenAI to teach courses, a more user-friendly interface makes it more likely for students to accept and choose this course.
In the results, C was found to be significantly positively correlated with PU (0.168) and PEOU (0.168).When using the GenAI tool for learning, LINT has a significant impact on PU and PEOU, with a path coefficient of 0.203.The use of programs through the menu bar to interact with the GenAI system has a significant impact on students' acceptance.The results indicate that AIIM, AIRD, AICF, AIAX, UI, C and LINT all influence students' attitudes towards learning AI.Among students' cognitive factors.AIIM has the greatest effect on PU, and among human HCI factors, LINT has the greatest effect on PEOU.

Discussion
The discussion section of this study offers an in-depth analysis of K-12 students' attitudes towards AI courses facilitated by generative AI (GenAI) tools.The examination is structured into three key segments, each focusing on distinct aspects of the research.Initially, the study investigates how cognitive factors related to AI learning influence perceived usefulness (PU) and perceived ease of use (PEOU).Subsequently, it explores the impact of human-computer interaction (HCI) factors on PU and PEOU.Finally, it delves into the interplay between PU, PEOU, and attitude towards use (ATT).Building upon established theoretical frameworks, such as the technology acceptance model (TAM), this study introduces a novel conceptual model tailored to assess K-12 students' attitudes towards using GenAI tools in AI in education (AIED) courses.By incorporating both cognitive learning factors and HCI elements, the study extends the existing literature, offering a comprehensive understanding of the complex dynamics influencing students' attitudes towards AI education.
The empirical analysis conducted in this study validates the proposed model and hypotheses, thereby contributing to theoretical advancements in the field of AI4K12 education.However, it is crucial to contextualize these findings within the broader landscape of educational research.Previous studies, such as those by Almaiah and Almulhem (2018) 10 , Almaiah, Al-Khasawneh, and Althunibat (2020) 11 , and Almaiah and Al Mulhem (2020) 12 , have highlighted the critical challenges and success factors influencing the implementation and usage of e-learning systems.Drawing parallels between these studies and the current research can provide valuable insights into the unique considerations and obstacles associated with integrating innovative technologies, like GenAI, into educational settings.
From a practical perspective, the findings of this study underscore the potential of GenAI tools to enhance AIED methodologies.However, it is essential to recognize that variations in students' cognitive learning processes may impact their attitudes and efficacy towards learning.By leveraging cutting-edge technologies and implementing pedagogical strategies informed by self-determination theory, educators and system designers can create inclusive and engaging learning experiences that promote sustained student engagement and mastery of AI knowledge.
In terms of theoretical implications, the findings of this study contribute significantly to the existing body of knowledge in the field of artificial intelligence in education (AIED).By expanding upon Davis' technology acceptance model (TAM) with additional cognitive and human-computer interaction (HCI) factors, we have not only provided a more nuanced understanding of students' attitudes towards AI courses facilitated by generative AI (GenAI) tools but also enriched the theoretical framework guiding research in this domain.This augmentation of the TAM model with external variables derived from the literature review and previous research findings offers a more comprehensive perspective on the determinants of students' acceptance of AI4K12 courses.Furthermore, the empirical validation of this extended model through structural equation modeling adds robustness to its theoretical underpinnings and lays the groundwork for future research endeavors in the realm of AIED.
In conclusion, this study offers actionable insights for AIED policymakers, system developers, educators, and students, aiming to foster a superior AI learning experience for K-12 students.By addressing the complex interplay between cognitive factors, HCI elements, and attitudes towards AI education, this research contributes to the ongoing discourse surrounding the integration of GenAI tools in educational settings.

Conclusion
The advent of artificial intelligence (AI) represents both significant opportunities and challenges for society, as intelligent algorithms and robots increasingly assume roles across various sectors.As AI becomes more integrated into daily life, it becomes crucial for individuals to adapt to coexist with these technologies.This underscores the importance of early integration of AI in education (AIED) into student learning, necessitating pioneering research in AIED-centric pedagogy for the K-12 demographic.
This study delves into K-12 students' perceptions of learning AI-related content through generative AI (GenAI) tools.Through an extensive literature review, the study identifies external factors shaping students' attitudes towards learning and applies the technology acceptance model (TAM), integrating it with theories of cognitive learning and human-computer interaction (HCI).With the participation of 210 Chinese K-12 students, this work stands as a significant contribution to the field.The analysis validates ten hypotheses, demonstrating the substantial impact of cognitive and behavioral learning factors, alongside HCI considerations, on students' attitudes towards AI education.These findings offer crucial insights for AIED policymakers and developers, informing the creation of diverse and engaging AI4K12 curricula aimed at sustaining students' interest in AI and promoting ongoing engagement and acquisition of intricate AI knowledge.However, this study has its limitations.The predominantly Chinese sample may not fully represent the global student body, and the study does not comprehensively cover all K-12 age groups.Future research should encompass a broader spectrum of K-12 grade levels, span multiple countries and regions, and explore gender and grade-level variations among students.Additionally, reliance on a single experimental course approach and online quantitative data collection may not fully capture the nuances of students' attitudes.Future investigations should integrate qualitative methodologies, such as semi-structured interviews and group discussions, for deeper insights.
The results of this study underscore the importance of considering both cognitive learning factors and HCI elements in designing and implementing AI courses in K-12 education.Our findings suggest that enhancing students' perceptions of usefulness and ease of use, while addressing potential anxiety associated with AI, is crucial for fostering positive attitudes towards AI education.By integrating GenAI tools into the curriculum, educators can create more engaging and effective learning experiences for students, thereby promoting the development of essential AI literacy skills.Moreover, our study sheds light on the complex interplay between cognitive and HCI factors in shaping students' attitudes towards AI education, highlighting the need for a holistic approach to curriculum design.Furthermore, recent studies have contributed to the development of scales aimed at measuring artificial intelligence literacy and acceptance, providing valuable tools for researchers and educators to assess students' readiness and attitudes towards AI education [20][21][22] .
In conclusion, this research offers a thorough examination of K-12 students' attitudes towards AI education using GenAI tools, focusing on learning cognition and HCI factors.Future endeavors should explore additional factors affecting AI learning acceptance, including various aspects of the learning environment, and examine students' AI learning experiences from diverse cognitive viewpoints.
In addition to the research findings and limitations discussed above, it is essential to consider the practical and theoretical implications of this study.Practically, the findings offer valuable insights for educators, policymakers, and developers involved in AI education for K-12 students.By identifying the cognitive and HCI factors that influence students' attitudes towards AI education using GenAI tools, this research provides a roadmap for designing more effective and engaging AI4K12 curricula.Educators can leverage these insights to tailor their teaching approaches and course designs to better meet students' needs and preferences, ultimately fostering a more positive learning experience.
Moreover, policymakers can use this research to inform decisions regarding the integration of AI education into school curricula, ensuring that students are adequately prepared for the future workforce.From a theoretical perspective, this study contributes to the existing body of literature on AI education and technology acceptance by extending the TAM framework to include cognitive and HCI factors specific to GenAI tools.By validating the proposed model and hypotheses, this research advances our understanding of the complex interplay between individual perceptions, cognitive processes, and technological interfaces in the context of AI education.Furthermore, the inclusion of HCI factors underscores the importance of considering user experience and interface design in educational technology development, highlighting the need for a more holistic approach to AI education research.Overall, the practical and theoretical implications of this study underscore its significance and provide a foundation for future research in the field of AI education.

Table 1 .
Course design.Understand and learn the concept of visual narrative, have a specific understanding of picture books and comic strips, and explain the theme of this activity as AI, future life, and an equal world Students try to determine their own picture book themes through communication, and then use intelligent search engines, ChatGPT, and Midjourney to create picture books that match text.When encountering problems during the creative process, students and teachers will also share and communicate with each other 8 After the completion of the work, the teacher communicates with the students and explores the gains and problems discovered during the course learning process Finally, complete the course feedback and students will fill out the AI questionnaire Vol:.(1234567890)Scientific Reports | (2024) 14:13922 | https://doi.org/10.1038/s41598-024-64363-3www.nature.com/scientificreports/Questionnaire collection and demography Following the course completion, students anonymously and voluntarily completed a questionnaire survey.The questionnaire was administered via the Chinese online platform, question star, resulting in 210 responses.Postsorting, 13 responses were deemed invalid, leaving 197 valid ones.Demographic variables underwent frequency analysis utilizing SPSS 26 software, revealing a distribution of 86 boys (43.7%) and 111 girls (56.3%).Among them, 65 students were in seventh grade (33%), 70 in eighth grade (35.5%), and 62 in ninth grade (31.5%).

Table 2 .
Specific content and reference materials of the survey questionnaire.

Table 3 .
Demographic information of students.

Table 4 .
Reliability analysis results.

Table 7 .
Discriminant validity: Heterotrait-Monotrait ratio (HTMT).Relationship between PU, PEOU, and ATTWe gained the following conclusion.PU has a positive impact on ATT (0.208).PEOU has a positive impact on PU (0.177).And PEOU has a positive impact on ATT (0.228).Previous research in the field of education has focused on the use of mobile devices and online courses.While the results of this study indicate that those factors are also applicable to the study of AI course acceptance by GenAI.

Table 8 .
Hypothesis test results of SEM.